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Existing automatic prompt engineering methods are typically designed for discriminative tasks, where new task prompts are iteratively refined with limited feedback from a single metric reflecting a single aspect. However, these approaches are suboptimal for generative tasks, which require more nuanced guidance beyond a single numeric metric to improve the prompt and optimize multiple aspects of the generated text. To address these challenges, we propose a novel multi-aspect Critique-Suggestion-guided automatic Prompt Optimization (CriSPO) approach. CriSPO introduces a critique-suggestion module as its core component. This module spontaneously discovers aspects, and compares generated and reference texts across these aspects, providing specific suggestions for prompt modification. These clear critiques and actionable suggestions guide a receptive optimizer module to make more substantial changes, exploring a broader and more effective search space. To further improve CriSPO with multi-metric optimization, we introduce an Automatic Suffix Tuning (AST) extension to enhance the performance of task prompts across multiple metrics. We evaluate CriSPO on 4 state-of-the-art Large Language Models (LLMs) across 4 summarization and 5 Question Answering (QA) datasets. Extensive experiments show 3-4% ROUGE score improvement on summarization and substantial improvement of various metrics on QA.

AAAI 2025

CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation

snlp

generation

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EMNLP 2024

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technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



In this paper, we augment online algorithms for the knapsack problem using the total weight information. The conventional optimal online algorithm achieves the $\ln (\frac{U}{L})+1$ competitive ratio where $L$ and $U$ are the upper and lower bounds of the value-to-weight ratio. However, it does not consider that decision makers can know the total weight information or obtain it through machine-learned predictions. To fill this gap, we first propose the Known Weight Algorithm (KWA) which uses the exact total weight information to achieve a competitive ratio of $\mathcal{W}(\frac{1}{e}\frac{U-L}{L})+1$, where $\mathcal{W}$ denotes the Lambert-W function. We prove that it is optimal and tight. After that, we extend KWA to the Predicted Weight Algorithm (PWA), a learning-augmented online algorithm that uses predicted total weight. We show the consistency and robustness of PWA, and prove that its competitive ratio degrades gracefully as the prediction error grows. Finally, we introduce the Limited Volume Algorithm (LWA), which achieves a better competitive ratio than $\ln \frac{U}{L}+1$ when the total weight is less than twice the capacity.

Augmenting Online Algorithms for Knapsack Problem with Total Weight Information

Multi-agent pathfinding (MAPF) is a challenging computational problem that generally asks to find collision-free paths for multiple agents in a shared environment. Solving MAPF optimally is NP-hard, yet efficient solutions are critical for numerous applications, including automated warehouses and transportation systems. Recently, learning-based approaches to MAPF have gained attention, particularly those leveraging deep reinforcement learning. Following current trends in machine learning, we have created a foundation model for the MAPF problems called MAPF-GPT. Using imitation learning, we have trained a policy on a set of pre-collected sub-optimal expert trajectories that can generate actions in conditions of partial observability without additional heuristics, reward functions or communication with other agents. The resulting MAPF-GPT model demonstrates zero-shot learning abilities when solving the MAPF problem instances that were not present in the training dataset. We show that MAPF-GPT notably overpasses the current best-performing learnable-MAPF solvers on a diverse range of problem instances and is efficient in terms of computation (in the inference mode).

MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale

Temporal pattern matching tasks require the detection of situations of interest based on streams of symbolic events. The Run-Time Event Calculus (RTEC) is a formal framework that represents situations of interest as time-varying properties called 'fluents'. Temporal patterns often express 'Boolean combinations' of situations; RTEC features two types of fluents that may model such patterns: 'simple' and 'statically determined'. A simple fluent representation, however, is exponentially larger and more expensive to reason with than the corresponding statically determined fluent one. We formally identify the class of simple fluent definitions that can be translated into statically determined fluent definitions. Moreover, we present a compiler for the translation, and a reproducible empirical evaluation on real applications.

Temporal Specification Optimisation for the Event Calculus

Prosody contains rich information beyond literal words, which is crucial for the intelligibility of speech. Current models still fall short in phrasing and intonation; they not only miss or misplace breaks when synthesizing long sentences with complex structures but also produce unnatural intonation. We propose ProsodyFM, a Prosody-aware text-to-speech synthesis (TTS) model with a flow-matching (FM) backbone that aims to enhance the phrasing and intonation aspects of prosody. ProsodyFM introduces two key components: a Phrase Break Encoder to capture initial phrase break cues, followed by a duration predictor to realize flexible adjustment of break durations; and a Terminal Intonation Encoder which integrates a set of intonation shape tokens combined with a novel pitch processor for more robust modeling of human-perceived intonation change. ProsodyFM is trained with no explicit prosodic labels and yet can uncover a broad spectrum of break duration and intonation patterns. Experimental results demonstrate that ProsodyFM can effectively improve the phrasing and intonation aspects of prosody, thereby enhancing the overall intelligibility compared to four state-of-the-art (SOTA) models. Out-of-distribution experiments show that this prosody improvement can further bring ProsodyFM superior generalizability for unseen complex sentences and speakers. Our case study intuitively illustrates the powerful and fine-grained controllability of ProsodyFM over phrasing and intonation. Our demo and code are available at {https://anonymous.4open.science/r/Demo-586C}.

ProsodyFM: Unsupervised Phrasing and Intonation Control for Intelligible Speech Synthesis

It is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite, and that this bounded formulation can be polynomially compiled into classical planning with Boolean conditions and conditional effects, while preserving the plan size exactly. However, whether this compilation has any practical use remains unknown. To explore this aspect, this work revisits the theoretical compilation framework from a practical perspective, focusing on the fragment of simple numeric planning. Specifically, we introduce three different compilations. The first, called \emph{onehot}, aims to systematise the current practice among planning practitioners of modelling numeric planning through classical planning. The other two, termed \emph{binary} compilations, extend and specialise the logarithmic encoding presented in the literature. Our experimental analysis reveals that the supposedly overkill logarithmic encoding can, surprisingly, be made practical with some representational expedients; the use of axioms is particularly crucial. Furthermore, we identify a class of mild numeric planning problems where a classical planner, i.e., LAMA, when run on the compiled problem, is highly competitive with state-of-the-art numeric planners.

Towards Practical Classical Planning Compilations of Numeric Planning

The \textsc{Mutliagent Path Finding} (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destinations as soon as possible, but without colliding with each other. We aim to minimize the maximum time any agent takes to reach their goal, ensuring optimal path length. In this work, we complement a recent thread of results that aim to systematically study the algorithmic behavior of this problem, through the parameterized complexity point of view.

First, we show that MAPF is NP-hard when the given network has a star-like topology (bounded vertex cover number) or is a tree with $11$ leaves. Both of these results fill important gaps in our understanding of the tractability of this problem that were left untreated in the recent work of [Fioravantes et al. Exact Algorithms and Lowerbounds for Multiagent Path Finding: Power of Treelike Topology. AAAI'24]. Nevertheless, our main contribution is an exact algorithm that scales well as the input grows (FPT) when the topology of the given network is highly centralized (bounded distance to clique). This parameter is significant as it mirrors real-world networks. In such environments, a bunch of central hubs or nodes (e.g., processing areas) are connected to peripheral nodes.

Solving Multiagent Path Finding on Highly Centralized Networks

Audio classification technology plays a crucial role in fields such as human-computer interaction and intelligent robotics. However, high-precision audio classification models typically demand significant computational and storage resources, posing substantial challenges when deploying to edge devices with limited performance but an urgent need for such capabilities. To achieve a new level of balance between model complexity and performance, we propose a novel multiview method for parallel feature extraction and apply it in conjunction with channel attention within a lightweight convolutional neural network. The proposed method exists within the network in the form of a mirrored attention mechanism. It enables reversible spatial transformations of spectral features and efficiently leverages robust local and global features in the time-frequency domain with a minimal number of parameters, significantly enhancing the utilization of key information. Experimental results based on Mel-Spectrogram features for audio classification demonstrate that the proposed method achieves accuracy exceeding 97% on the UrbanSound8K and SpeechCommandsV2 datasets with a total parameter count being only 0.03M. Finally, in the ablation and reverse ablation experiments, we fully verified and explained the contribution of each novel segment in proposed method. Our code has been uploaded to the supplementary material and will be published on GitHub afterward.

M3Net: Efficient Time-Frequency Integration Network with Mirror Attention for Audio Classification on Edge

Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject commonalities. Recent advancements have explored multisubject methods, but these approaches face significant challenges, particularly in data privacy and effectively managing individual variability. To overcome these challenges, we introduce BrainGuard, a privacy-preserving collaborative training framework designed to enhance image reconstruction from multisubject fMRI data while safeguarding individual privacy. BrainGuard employs a collaborative global-local architecture where personalized models are trained on each subject's data and operate in conjunction with a shared commonality model that captures and leverages cross-subject patterns. This architecture eliminates the need to aggregate fMRI data across subjects, thereby ensuring privacy preservation. To tackle the complexity of fMRI data, BrainGuard integrates a hybrid synchronization strategy, enabling individual models to dynamically incorporate parameters from the global model. By establishing a secure and collaborative training environment, BrainGuard not only protects sensitive brain activity data but also improves the accuracy of image reconstructions. Extensive experiments demonstrate that BrainGuard sets a new benchmark in both high-level and low-level metrics, advancing the state-of-the-art in brain decoding through its innovative design.

BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities

Deep online cross-modal hashing has gained much attention from researchers recently, as its promising applications with low storage requirement, fast retrieval efficiency and cross modality adaptive, etc. However, there still exists some technical hurdles that hinder its applications, e.g., 1) how to extract the coexistent semantic relevance of cross-modal data, 2) how to achieve competitive performance when handling the real time data streams, 3) how to transfer the knowledge learned from offline to online training in a lightweight manner. To address these problems, this paper proposes a lightweight contrastive distilled hashing (LCDH) for cross-modal retrieval, by innovatively bridging the offline and online cross-modal hashing by similarity matrix approximation in a knowledge distillation framework. Specifically, in the teacher network, LCDH first extracts the cross-modal features by CLIP, which are further fed into an attention module for representation enhancement after feature fusion. Then, the output of the attention module is fed into a FC layer to obtain hash codes for aligning the sizes of similarity matrices for online and offline training. In the student network, LCDH extracts the visual and textual features by lightweight models, and then the features are fed into a FC layer to generate binary codes. Finally, by approximating the similarity matrices, the performance of online hashing in the lightweight student network can be enhanced by the supervision of coexistent semantic relevance that is distilled from the teacher network. Experimental results on three widely used datasets demonstrate that LCDH outperforms some state-of-the-art methods.

Lightweight Contrastive Distilled Hashing for Online Cross-modal Retrieval

We consider committee election of $k \geq 3$ (out of $m \geq k + 1$) candidates, where the voters and the candidates are associated with locations on the real line. Each voter’s cardinal preferences over candidates correspond to her distance to the candidate locations, and each voter’s cardinal preferences over committees is defined as her distance to the nearest candidate elected in the committee. We consider a setting where the true distances and the locations are unknown. We can nevertheless have access to degraded information which consists of an order of candidates for each voter. We investigate the best possible distortion (a worst-case performance criterion) wrt. the social cost achieved by deterministic committee election rules based on ordinal preferences submitted by $n$ voters and few additional distance queries. We show that for any $k \geq 3$, the best possible distortion of any deterministic rule that uses at most $k − 3$ distance queries cannot be bounded by any function of $n$, $m$ and $k$. We present deterministic rules for $k$-committee election with distortion of $O(n)$ with $O(k)$ distance queries and $O(1)$ with $O(k \log n)$ distance queries.

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Augmenting Online Algorithms for Knapsack Problem with Total Weight Information

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